Courses Financial Computing with Python
Course 2Coming soon
Financial Computing with Python
The Python of quantitative work: NumPy and pandas, numerical methods, a valuation library you build yourself, and production-quality code.
- 4 parts
- 24 modules
- Before you start: Part 1: basic Python only. Parts 2 to 4: Foundations Levels 1, 2 and 5
Coming soon
Explore the syllabus below.
How each module works
- 1The conceptOne technique at a time
- 2Graded exercisesShort exercises that check your code
- 3Project milestoneA library that grows across the course
1Coming soon
Part 1
Python for quant work
The Python every course assumes beyond the basics: tools, idioms, NumPy, pandas, plots, design and testing. Needs only basic Python.
1.1ToolingComing soon
- You will learn:
- Virtual environments, Jupyter, Colab, An editor, Project layout.
- You will build:
- Set up a reproducible project
1.2Python idiomsComing soon
- You will learn:
- Data structures, Comprehensions, Generators, Functions as objects, Dates and times.
- You will build:
- A dated cash-flow schedule
1.3NumPyComing soon
- You will learn:
- Arrays, Broadcasting, Vectorisation, Random numbers.
- You will build:
- Vectorised payoffs for a million paths
1.4pandas and financial time seriesComing soon
- You will learn:
- Indexing, Resampling, Rolling windows, Joins, Returns.
- You will build:
- Returns and rolling volatility from prices
1.5VisualisationComing soon
- You will learn:
- Time-series plots, Distributions, Surfaces.
- You will build:
- Plot a volatility surface
1.6Object-oriented designComing soon
- You will learn:
- Classes, Dataclasses, Composition, Abstract base classes, Protocols.
- You will build:
- Project 1: an instrument and payoff class hierarchy
1.7Clean codeComing soon
- You will learn:
- Type hints, pytest, Docstrings, Logging, Packaging.
- You will build:
- A test suite for Project 1
2Coming soon
Part 2
Numerical methods
Roots, optimisation, Fourier pricing, Monte Carlo, trees and finite differences in code, and making them fast.
2.1Roots and interpolationComing soon
- You will learn:
- Bisection, Newton, Brent, Splines.
- You will build:
- An implied-volatility solver
2.2Optimisation and calibrationComing soon
- You will learn:
- scipy.optimize, Least squares, Constraints.
- You will build:
- Fit a volatility smile
2.3Integration and Fourier pricingComing soon
- You will learn:
- Quadrature, Characteristic functions, Fourier option pricing.
- You will build:
- Merton jump-diffusion calls by Fourier
2.4Monte CarloComing soon
- You will learn:
- Path simulation, Variance reduction.
- You will build:
- Project 2: a Monte Carlo pricing engine
2.5TreesComing soon
- You will learn:
- Binomial and trinomial trees, Early exercise.
- You will build:
- American put by tree
2.6Finite differencesComing soon
- You will learn:
- Explicit, Implicit, Crank–Nicolson, Stability.
- You will build:
- European and American options by PDE
2.7PerformanceComing soon
- You will learn:
- Profiling, Vectorisation, Numba, Multiprocessing (Colab).
- You will build:
- Benchmark and speed up the Monte Carlo engine
3Coming soon
Part 3
A valuation library
A library for simulation, valuation, risk and hedging that grows across the part. The Derivatives labs import it.
3.1Valuation frameworkComing soon
- You will learn:
- Market environments, Discounting, Dates.
- You will build:
- Design the library's core
3.2Simulation classesComing soon
- You will learn:
- GBM, Jump diffusion, Square-root diffusion, Correlated risk factors.
- You will build:
- Simulation classes with tests
3.3Valuation classesComing soon
- You will learn:
- European and American (Longstaff–Schwartz) valuation.
- You will build:
- Project 3: the valuation module
3.4Portfolio valuation and riskComing soon
- You will learn:
- Positions, Portfolios, Greeks by bumping, Scenarios.
- You will build:
- A portfolio risk report
3.5Market-based valuationComing soon
- You will learn:
- Calibrating Merton, Heston and Bates models to option quotes.
- You will build:
- Calibrate to a quote set
3.6Dynamic hedgingComing soon
- You will learn:
- Hedging simulations and hedge error.
- You will build:
- Hedge an American option
4Coming soon
Part 4
Data and production
Storing market data, releasing the library as a package and the basics of trading infrastructure, ending with the capstone.
4.1Data storage and APIsComing soon
- You will learn:
- CSV, Parquet, SQL, REST APIs.
- You will build:
- A small market-data store
4.2From notebook to packageComing soon
- You will learn:
- Modules, Command-line tools, CI, Documentation.
- You will build:
- Release the library as a package
4.3Trading infrastructure basicsComing soon
- You will learn:
- Event loops, Streaming data, Broker APIs.
- You will build:
- A paper-trading loop on simulated data
4.4Capstone: a pricing and risk packageComing soon
- You will learn:
- Tested, Documented, Benchmarked.
- You will build:
- Graded with a checklist